View of Geochemical background and threshold for 47 chemical elements in Slovenian topsoil

(1)

Geochemical background and threshold for 47 chemical elements in Slovenian topsoil

Geokemično ozadje in zgornja meja naravne variabilnosti 47 kemičnih elementov v zgornji plasti tal Slovenije

Mateja GOSAR¹, Robert ŠAJN¹, Špela BAVEC¹, Martin GABERŠEK¹, Valentina PEZDIR² & Miloš MILER¹

1Geološki zavod Slovenije, Dimičeva ulica 14, SI–1000 Ljubljana, Slovenija;

e-mail: mateja.gosar@geo-zs.si, robert.sajn@geo-zs.si, spela.bavec@geo-zs.si, martin.gabersek@geo-zs.si, milos.miler@geo-zs.si

2Breg pri Borovnici 46, SI-1353 Borovnica, Slovenija; e-mail: valentina.pezdir@gmail.com

Prejeto / Received 10. 1. 2019; Sprejeto / Accepted 22. 2. 2019; Objavljeno na spletu / Published online 12. 3. 2019 Key words: soil, lithology, elements, geochemical background, geochemical mapping, geochemical

threshold, Slovenia

Ključne besede: tla, litologija, elementi, geokemično ozadje, geokemično kartiranje, zgornja meja naravne variabilnosti, Slovenija

Abstract

Geochemical background and threshold values need to be established to identify areas with unusually high concentrations of elements. High concentrations are caused by natural or anthropogenic processes. The <2 mm fraction of 817 collected topsoil (0 – 10 cm) samples at a 5 × 5 km grid on the territory of Slovenia was analysed.

Results are used here to establish the geochemical background variation and threshold values, derived statistically from the data set, in order to identify unusually high element concentrations for these elements in the soil samples.

Geochemical threshold values were determined following different methods of calculation for (1) whole of Slovenia and (2) for 8 spatial units determined on the base of geological structure, lithology, relief, climate and vegetation.

Medians and geochemical thresholds for whole of Slovenia were compared with data for Europe and for southern Europe separately, since large differences in the spatial distribution of many elements are observed between northern and southern Europe. Potentially toxic elements (PTEs), namely As, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pb, Sb, and Zn, are of particular interest. Medians of these PTE elements are all higher in Slovenia than in southern Europe. Medians of Pb and Mo are 1.5 times higher and medians of Hg and Cd are even more than 2 times higher in Slovenia. Geochemical thresholds for As, Cr, Co, Ni, Sb and Zn are of similar values in both Slovenia and southern Europe and some lower for Cu and Ni. Up to 1.5 times higher are tresholds in Slovenia for Mo and Pb and more than 2.5 times higher for Cd and Hg. These values were then compared to existing Slovenian soil guideline values for these elements.

Izvleček

Kemični elementi so v okolju, torej tudi v tleh, naravno prisotni. Povišane vsebnosti le-teh so posledica naravnih danosti ali pa jih povzročijo človekove dejavnosti. Območja povišanih koncentracij elementov so opredeljena kot območja, na katerih vsebnosti elementov presegajo vrednosti geokemičnega praga (zgornjih mej naravne variabilnosti - MNV). Na podlagi kemičnih analiz 817 vzorcev zgornje plasti tal (0–10 cm), odvzetih v mreži 5 × 5 km na območju celotne Slovenije, smo izračunali mediane (geokemično ozadje) in zgornje meje naravne variabilnosti (MNV) po več metodah za celotno Slovenijo in za 8 manjših prostorskih enot, ki smo jih določili glede na geološko zgradbo, kamninsko sestavo, relief, podnebje in rastlinstvo. Znotraj posameznih manjših prostorskih enot se izračunane MNV po različnih metodah močno razlikujejo zaradi heterogenosti enot in majhnega števila vzorcev. Mediane in zgornje meje naravne variabilnosti za celotno Slovenijo smo primerjali s podatki za celotno Evropo in še posebej južno Evropo, ker se prostorske porazdelitve elementov med južno in severno Evropo močno razlikujejo. Zanimive so vsebnosti potencialno strupenih elementov (As, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pb, Sb, Zn) in primerjava z mejnimi, opozorilnimi in kritičnimi vrednostmi za tla po slovenski zakonodaji. Mediane teh elementov so v Sloveniji višje kot v celotni Evropi in v južni Evropi. Primerjava z južno Evropo kaže, da sta mediani Pb in Mo 1,5 krat višji, mediani Cd in Hg pa celo več kot 2 krat višji v Sloveniji. V Sloveniji so MNV blizu vrednostim v južni Evropi za elemente As, Cr, Co, Ni, Sb in Zn, malo nižje za Cu in Ni, do 1,5 krat višje za elementa Mo in Pb ter več kot 2,5 krat višje za Cd in Hg.

(2)

Fig. 1. Geochemical background and anomalies (after Gałuszka et al., 2014).

Sl. 1. Geokemično ozadje in anomalije (po Gałuszka et al., 2014).

Introduction

Chemical elements are in environment, as well as in soil, present naturally. High element concentrations in environment may be due to occurrence of mineralization, unusual rock types, such as serpentinite, black shale mudstone, etc., or may be caused by human activities (mining, metallurgy, industry, traffic, agriculture, etc.).

Depending on bioavailability and stability of the material in which the chemical elements appear, their high concentration levels may present environmental risk due to element toxicity.

Anthropogenic chemical contamination is one of the most evident signals of human influence on the environment. The large amounts of indus- trially produced pollutants that have been intro- duced, over decades, into air, soil and water have caused modifications to natural elemental cy- cling (Gałuszka et al., 2014). Anthropogenic contamination usually leads to enrichment in many elements, particularly in industrial areas. Cer- tain elements and their isotopes can therefore be used as geochemical indicators of anthropogenic impact. There are also secondary effects of the pollution, such as acidification, which causes in- creased geochemical mobility of elements in sur- ficial deposits. Methods used in geochemistry to assess the scale of anthropogenic influence on the environment include determination of geochemical background and thresholds, calculation of enrichment and contamination factors, geoaccu- mulation index and pollution load index. The use of geochemical background levels to distinguish between natural and anthropogenic pollution is important (fig. 1 and 2) (Gałuszka et al., 2014).

To identify areas with unusually high (or low) concentrations of “potentially toxic elements”

(PTEs), geochemical background and threshold values of these elements need to be determined.

Uvod

Kemični elementi (prvine) so v okolju, torej tudi v tleh, naravno prisotni. Njihove povišane vsebnosti v okolju so lahko posledica naravnih danosti (pojavljanje mineralizacij oziroma oru- denj in kamnin z naravno visokimi vsebnostmi nekaterih elementov, kot so na primer serpenti- nit, črni skrilavi glinavci, itd.), ali pa jih povzro- čijo človekove dejavnosti (rudarstvo, metalurgija, industrija, promet, kmetijstvo, itd.). Odvisno od obstojnosti zvrsti, v katerih kemični elementi nastopajo, lahko njihove povišane vsebnosti predstavljajo okoljska tveganja zaradi biodostopnosti škodljivih elementov.

Antropogena kemična kontaminacija je eden najbolj očitnih znakov človekovega vpliva na okolje. Dolgoletno delovanje različnih indu- strij, prometa in drugih človekovih dejavnosti so povzročili povišanje vsebnosti nekaterih elementov v površinskih materialih (tla, sedimenti, itd.) in spremembe naravnega kroženja elementov (Gałuszka et al., 2014). Antropogeni vplivi navadno vodijo v obogatitev številnih elementov, še zlasti na industrijskih območjih. Nekateri elementi in njihovi izotopi se tako lahko uporabljajo kot geokemični indikatorji antropogenega vpliva. Poznamo tudi sekundarne učinke onesnaže- nja, kot je na primer zakisljevanje, ki povzroča povečano geokemično mobilnost elementov v površinskih materialih. Metode, ki jih geokemi- ki uporabljamo za oceno obsega antropogenega vpliva na okolje, vključujejo opredelitev ravni geokemičnega ozadja in mej naravne variacije, izračune obogatitvenih razmerij, geoakumula- cijskih indeksov in indeksov onesnaženja. Še posebej pomembna je uporaba geokemičnih ravni ozadja za ločitev naravnega in antropogenega deleža onesnaženja (sl. 1 in 2) (Gałuszka et al., 2014).

(3)

Fig. 2. Systematics of geochemical anomalies (after Gałuszka et al., 2014).

Sl. 2. Sistematika geokemičnih anomalij (po Gałuszka et al., 2014).

Geochemical threshold values are used to identify locations with unusually high element concentrations. A lower threshold, determined in the lower part of the data distribution, is used to identify locations with unusually low element concentrations. Deficiency of certain elements in the soil can present a problem to living organisms in those environments (Reimann et al., 2018). In this work we focused solely on upper threshold and did not discuss the lower threshold.

After identification of areas with unusually high element concentrations, risk assessment must be determined in these areas. Risk assessment of soil determines whether the high element concentrations pose a threat to living organisms or the environment. It is dependent from elements, as certain elements are toxic at low concentrations and other elements are biologically essential, but harmful at higher concentration levels (Reimann et al., 2018). Proper risk assessment of soil includes comparison of determined element concentration values with effect thresholds for environmental and human health derived from (eco)toxicological data. This approach preferentially takes into account the effect of abiotic soil properties (such as mineral composition, structure and texture of the soil, water and air present in the soil) on bioavailability and toxicity of the element (examples in Smolders et al. (2009), Oorts & Schoeters (2014), Oorts et al. (2016) or Birke et al. (2016)). Proper risk assessment of certain location also requires

S pomočjo opredelitve vrednosti mej naravne variabilnosti za posamezne elemente se dolo- ča območja z nenavadno visoko (ali nizko) koncentracijo “potencialno strupenih elementov” (v nadaljevanju PTE – potentially toxic elements).

Geokemični prag, ki je definiran kot zgornja meja naravne variabilnosti, se uporablja za določitev območij z nenavadno visoko koncentracijo elementov. Zanimiva je tudi spodnja meja naravne variabilnosti, ki je definirana v spodnjem delu porazdelitve geokemičnih podatkov in se uporablja za določitev območij z nenavadno nizko koncentracijo elementov, saj lahko tudi pomanjkanje nekaterih elementov v tleh povzroča težave živim bitjem (Reimann et al., 2018). Spodnja meja naravne variabilnosti ne sodi v vsebino tega dela, zato je v nadaljevanju ne bomo obravnavali.

Območja z nenavadno visokimi koncentraci- jami elementov v tleh je potrebno raziskati s po- sebno študijo, imenovano ocena tveganja, s katero ugotavljamo, ali te nenavadno visoke vsebnosti elementa lahko škodujejo okolju oz. živim bitjem. Nekateri elementi so namreč potencialno strupeni že v nižjih vsebnostih, drugi pa so bi- ološko nujno potrebni, vendar njihove previsoke vsebnosti lahko škodujejo živim bitjem (Reimann et al., 2018). Pravilna ocena tveganja vključuje primerjavo izmerjenih koncentracij elementov z vrednostmi elementa, ki negativno učinkujejo na okolje in zdravje ljudi na podlagi ekotoksiko- loških raziskav. Ta pristop prednostno upošteva

(4)

a substantial amount of additional data, such as bioavailability of elements, acidity (pH), grain size, cation exchange capacity and total organic carbon. Additional data must be available for each determined location with high element concentrations. Identifying geochemical (non-toxicological) threshold can simply be defined as a value above which the concentration of an element in a given data set is “unusually high”. With this approach we separate locations that require attention and further analysis and studies (Reimann et al., 2018).

Unusually high element concentrations in the upper soil layer can be due to anthropogenic activities, such as urbanization, industrial activities, mining and agricultural practices. They may also be of natural origin and indicate areas with geochemically unusual rock types or areas having a high potential for the occurrence of mineral deposits (Reimann et al., 2018). The separation of these three distinct causes for high element concentrations in soil requires substantial expert knowledge about the location of possible contamination sources (cities, metal smelters, power plants, industry), climate, vegetation zones, geology, element dispersion processes, mineral deposits etc. (Reimann et al., 2018).

Materials and methods

Soil as sample material in geochemistry Soils are an unique natural resource essential for food production and an irreplaceable compo- nent of natural ecosystems. Due to numerous environmental, economic, social and cultural func- tions (the multifunctionality of soils), soils are of crucial importance for life in terrestrial ecosystems (Vidic et al., 2015).

Soils represent the upper part of Earth’s crust that consist of mineral particles, organic matter, water, air and living organisms (FitzPatric, 1986). They are indispensable to humanity and to maintaining a healthy natural environment.

Soil formation is a slow process. Soils form as a result of lithosphere weathering due to inter- actions of pedogenetic factors, which are lithological parent material, climate, relief, time and living organisms. Lithological parent material provides the original quantity of mineral material (with exception of carbonate rocks), from which soils are composed. It also influences thickness of the soil, physical, mineral and chemical attributes and further development of the soil (FitzPatrick, 1986). Climate influences soil development with solar radiation and dynamic processes in the at- mosphere, which have an impact on humidity, heat

učinek abiotskih lastnosti tal (kot so mineralna sestava, tekstura in struktura tal ter voda in zrak v tleh) na biološko dostopnost (primeri v Smol- ders et al. (2009), Oorts & Schoeters (2014), Oorts et al. (2016) ali Birke et al. (2016)). Za določitev ocene tveganja na določenem območju so dodat- no potrebni še drugi podatki o tleh, kot so biodo- stopni delež elementov, kislost (pH) in zrnavost tal, kationska izmenjevalna kapaciteta ter skupni organski ogljik. Tudi ti morajo biti na voljo za vsako obravnavano območje. Geokemično (ne to- ksikološko) zgornjo mejo naravne variabilnosti v obravnavanih tleh lahko preprosto določimo kot vrednost, nad katero je koncentracija elementa v tleh na podlagi danih podatkov “nenavadno viso- ka”. S tako določenimi zgornjimi mejami naravne variacije izdvojimo območja tal, ki zahtevajo več- jo pozornost in morda nadaljnje analize in študije (Reimann et al., 2018).

Nenavadno visoke koncentracije elementov v zgornjem sloju tal so lahko posledica antropoge- nih dejavnosti ali pa so naravnega izvora (Rei- mann et al., 2018). Za identifikacijo vzrokov visoke ravni elementov v tleh je potrebno zahtevno raz- iskovalno delo. Potrebno je izdelati kompleksno študijo, v kateri združujemo podatke o geoloških lastnostih (litološke značilnosti ozemlja, podatki o morebitnih orudenjih) in okoljskih značilnostih obravnavanega ozemlja, kot so npr. morebitni viri onesnaževanja (urbanizirana območja, kovinska industrija, termoelektrarne, druge vrste industri- je) ter informacije o podnebju, talnih in vegeta- cijskih značilnostih in podobno (Reimann et al., 2018).

Materiali in metode Tla kot vzorčni medij v geokemiji

Tla so edinstven naravni vir, ki je neposredno povezan s pridelavo hrane in splošno blaginjo, hkrati pa predstavljajo nenadomestljiv del naravnih ekosistemov. Zaradi številnih okoljskih, ekonomskih, socialnih in kulturnih funkcij so tla ključnega pomena za življenje v kopenskih ekosi- stemih (Vidic et al., 2015).

Tla predstavljajo zgornji del zemeljske skorje, ki ga sestavljajo mineralni delci, organska snov, voda, zrak in živi organizmi (FitzPatrick, 1986).

So zelo pomembna za človeštvo in za vzdrževanje zdravega naravnega okolja.

Tvorba tal je počasen proces. Tla nastajajo ob preperevanju litosfere zaradi medsebojnega delovanja tlotvornih (pedogenetskih) dejavnikov, kot so matična podlaga, podnebje, relief, čas in organizmi. Matična podlaga zagotavlja osnovno

(5)

and atmospheric deposition of particles. Organ- isms exchange substances and energy from lithological parent material and soils and thus directly affect soil development. The relief indirectly influences the formation of the soil by distribution of surface material and energy. Moving and retention potential of substances in the original location depend on the slope steepness. The relief also affects the thickness and humidity of the soil. Hu- mans also have an influence on soil development, either directly by agriculture, infrastructure and urbanization or indirectly by changing relief, water regime, vegetation and pollution, which can be of point or dispersed type. Soils have a high buffering capacity which relates to stability of the soil system and the pH of the soil and to the soil re- taining capacity. Thus, the content of water, mineral particles, gases as well as pollutants in the soil are regulated. However, the buffering capacity of the soil is not unlimited and therefore certain pollutants can exceed the retention or buffering capacity of the soil (FitzPatric, 1986).

Soil is a dynamic complex formation, in which biological, chemical and physical processes con- tinuously take place. It represents a complex ecosystem that enables plant growth and bioge- ochemical circulation of elements. Physical processes include decomposition of rocks into smaller particles without changing their mineral and chemical composition. Physical decomposition is caused by temperature changes, frost, wind, glaciation, plant roots activity and water. Due to physical decomposition, the specific particle size increases, allowing for faster chemical decomposition. Chemical processes are dissolution, hy- drolysis, hydration, oxidation or reduction, and the formation of clay and other minerals. Water, that contains dissolution of various gases and ac- ids, plays an important role in all these processes.

Most of the soil processes are of direct or indirect biological nature. Organisms are effective leach- ing factors in the dissolution of many elements.

Due to the extremely high reproduction rate of microorganisms, their effect can be significant and can be important in the migration of elements in the soil (Siegel, 2002).

The unique characteristic of the soils is the distribution of their components and features in layers, that are dependent on the present land- scape surface and that vary with depth. Migra- tion processes of particles, chemical elements and humus substances take place due to weathering, water and organisms in the soil. Thus, soil layers are formed, which differ in morphologi- cal features: colour, density of the roots, humus

količino mineralnega gradiva (izjema je karbonatna podlaga), iz katerega sestoje tla, in vpliva na debelino, na fizikalne, mineralne in kemične lastnosti ter na nadaljnjo smer njihovega razvoja (FitzPatrick, 1986). Podnebje vpliva na razvoj s sončnim sevanjem in z dinamičnimi procesi v at- mosferi, ki prenašajo vlago, toploto in vplivajo na atmosfersko odlaganje delcev. Živi svet izmenjuje z matično podlago in s tlemi snovi in energijo ter tako neposredno vpliva na razvoj tal. Relief posredno vpliva na oblikovanje tal s tem, da razpo- reja po površini snovi in energijo. Premeščanje ali zadrževanje snovi na prvotnem mestu je odvisno od strmine pobočja. Relief vpliva tudi na debelino in vlažnost tal. Na njihovo oblikovanje vpliva tudi človek. Neposredno z obdelovanjem, gradnjo infrastrukture in naselij, posredno pa s spremi- njanjem reliefa, vodnega režima, rastlinstva in z onesnaževanjem, ki je lahko točkovno ali razpr- šeno. Tla imajo veliko puferno sposobnost, ki se nanaša na stabilnost talnega sistema in pH tal ter na zadrževalno sposobnost tal. Tako se uravnava vsebnost vode, mineralnih delcev, plinov kot tudi onesnaževal v tleh. Puferna sposobnost tal pa ni neomejena in zato lahko določena onesnaževala tudi presežejo zadrževalno oz. puferno sposobnost tal (FitzPatrick, 1986).

Tla so dinamična kompleksna tvorba, v kateri ves čas potekajo biološki, kemični in fizikalni procesi. Predstavljajo zapleten ekosistem, ki omogoča rast rastlin in biogeokemično kroženje elementov.

Fizikalni procesi obsegajo razpadanje kamnine na manjše delce, pri čemer se njihova mineralna in kemična sestava ne spremenita. Fizikalno razpadanje povzročajo temperaturne spremembe, delovanje zmrzali, vetra, ledenikov, rastlinskih korenin in vode. Zaradi takega razpadanja se poveča specifična površina delcev, kar omogoča hitrejše kemično razpadanje. Kemični procesi so raztapljanje, hidroliza, hidratacija, oksidacija ali redukcija ter tvorba glinenih in drugih mineralov.

Pri vseh teh procesih ima pomembno vlogo voda, v kateri so raztopljeni različni plini in kemične snovi. Večina talnih procesov je posredno ali neposredno biološke narave. Organizmi so učinkovi- ti dejavniki izluževanja in raztapljanja številnih elementov. Zaradi izredno velike razmnoževalne hitrosti mikroorganizmov je njihov skupni učinek lahko znaten in je lahko pomemben v migraciji elementov v tleh (Siegel, 2002).

Edinstvena značilnost tal je razporeditev nji- hovih sestavin in lastnosti v plasteh, ki so odvisne od sedanjega površja in se spreminjajo z globino.

Zaradi preperevanja, delovanja vode ter organizmov v tleh potekajo procesi premeščanja delcev,

(6)

content, grains, humidity and other. Individual layers are called soil horizons. They were created in the process of soil formation and interaction between the layers. They can be from few cen- timeters to several meters thick. Together, they form a soil profile (Siegel, 2002).

Trace elements in the soils occur in primary minerals that originate from lithological parent material, in secondary newly formed minerals, and bound to clay minerals and organic matter.

In addition to geological and pedological features, soils also provide information on pollutants in the air, and are therefore a very useful and widespread sample medium.

A regional radiometric and geochemical survey was performed on the entire territory of Slo- venia during the period 1990–1993 by the Geo- logical Survey of Slovenia. Soil sampling was performed at a 5 × 5 km grid with a randomly selected starting point to ensure randomness of sampling (fig. 3) (Andjelov, 1994). In total, 817 topsoil (0–10 cm) samples were collected. The air dried samples were gently disaggregated in a ceramic mortar, sieved through a 2 mm sieve and stored. In 2012 the stored soil samples were taken out of the depot at the Geological Survey of Slovenia, pulverized in an agate mill to a fine- grain size (<0.075 mm) and submitted to chemical analysis in Bureau Veritas Mineral Labo- ratories at Vancouver, Canada. Samples were analysed with inductively coupled plasma (ICP) and mass spectrometry (MS) after digestion of an aliquot of 15 g sample material with aqua regia

Fig. 3. Sampling locations.

Sl. 3. Prikaz vzorčnih lokacij.

kemičnih elementov in humusnih snovi. Tako na- stanejo v tleh plasti, ki se razlikujejo po morfo- loških lastnostih: barvi, prekoreninjenosti, deležu humusa, deležu skeleta, vlažnosti in drugem. Po- samezne plasti imenujemo talni horizonti. Nastali so v procesu nastanka in razvoja tal v medseboj- ni odvisnosti. Debeli so od nekaj centimetrov do več metrov. Skupaj sestavljajo talni profil (Siegel, 2002).

Sledni elementi v tleh so vezani v obstojnih pr- votnih mineralih, ki izvirajo iz matične kamnine, v drugotnih, novonastalih mineralih, ter vezani na glinene minerale in organsko snov. Ker pa poleg geoloških in pedoloških značilnosti dajejo tudi informacijo o onesnaževalih v zraku, so tla zelo uporaben in razširjen vzorčni medij.

V letih 1990–1993 je Geološki zavod Slovenije izvedel regionalno vzorčenje tal celotnega ozemlja Slovenije za potrebe izdelave karte naravne radioaktivnosti. Tla so bila sistematično vzorčena v mreži 5 × 5 km, v kateri je bila merjena tudi na- ravna radioaktivnost (sl. 3) (Andjelov, 1994). Skup- no je bilo odvzetih 817 vzorcev zgornje plasti tal (0–10 cm), ki so bili posušeni in presejani na frak- cijo <2 mm. Del vzorcev je bil arhiviran v depoju GeoZS. Leta 2012 so bili vzorci vzeti iz depoja, zmleti v ahatnem mlinčku (<0,075 mm) in posredo- vani v kemične analize v Bureau Veritas Mineral Laboratories (Vancouver, Kanada). Vzorci so bili analizirani z metodo induktivno vezane plazem- ske masne spektrometrije (ICP-MS) po razklopu z modificirano zlatotopko (15 g vzorca so razto- pili v mešanici kislin HCl : HNO₃ : H₂O = 1 : 1 : 1).

(7)

(1 : 1 : 1 HCl : HNO₃ : H₂O). Concentrations of following 53 elements were determined: Ag, Al, As, Au, B, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Ge, Hf, Hg, In, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Pd, Pt, Rb, Re, S, Sb, Sc, Se, Sn, Sr, Ta, Te, Th, Ti, Tl, U, V, W, Y, Zn, Zr.

Quality control

The quality control of analyses was assured by several methods. Aliquots of Certified Reference Materials (CRM: OREAS 43P, OREAS 44P, ORE- AS 45P, OREAS 45CA), and sample replicates were included randomly into the sample batch- es to estimate accuracy and precision of chemical analyses. Analysed concentration values of standards were compared to the certified values, as well as the repetitions of analyses of standard materials and soil samples. With this we determined the accuracy (A) and precision (P) of used analytical methods for analysed chemical elements (figs. 4 and 5). Table 1 shows the numbers

Fig. 4. Accuracy of analytical method.

Sl. 4. Točnost analitske metode.

Fig. 5. Precision of analitical method.

Sl. 5. Natančnost analitske metode.

Določene so bile vsebnosti naslednjih 53 elementov: Ag, Al, As, Au, B, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Ge, Hf, Hg, In, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Pd, Pt, Rb, Re, S, Sb, Sc, Se, Sn, Sr, Ta, Te, Th, Ti, Tl, U, V, W, Y, Zn, Zr.

Presoja kakovosti analitike

Kakovost analitike smo ocenili na podlagi re- zultatov kemičnih analiz standardnih materialov (OREAS 43P, OREAS 44P, OREAS 45P, OREAS 45CA), katerih vsebnosti analiziranih elementov smo primerjali s priporočenimi vrednostmi, ter s ponovitvami analiz standardnih materialov in vzorcev tal. To je omogočilo oceno točnosti (A accuracy) in natančnosti (P precision) uporabljene analitske metode za analizirane kemične elemente (sl. 4 in 5). Naredili smo tudi pregled, v koliko vzorcih tal so vsebnosti obravnavanih kemičnih elementov pod mejo določljivosti (spodnja meja zaznavnosti) (tabela 1). Točnost (A accuracy) analitike ocenjujemo z relativno razliko med analit-

(8)

of soil samples having individual element concentrations below the lower detection limit. Ac- curacy (A) of analytics is evaluated as a relative difference between the analytical value of the element and its certified value. Analytical values of geological standard materials are compared with their certified values (Abbey, 1983; Reimann et al., 2009). Individual standards and their replicates were randomly distributed among the soil samples. Calculated relations between replicated values and their certified values are in fact the correction factor, by which analysed values could be divided in order to approach the certified values (Gosar, 2007).

Precision (P) of analytical methods is a meas- ure of repeatability of determining a parameter in the same sample standard material, regardless of deviation from the certified value (Rose et al., 1979; Reimann et al., 2009).

Chemical elements Ge, Pd, Pt, Re, Ta and W were eliminated from further discussion, because their concentrations in more than 30 % of the samples were below detection limit of the analytical methods (table 1). For other chemical elements, sensitivity, accuracy (A) and precision (P) of analytical methods were satisfactory (table 1). They were included in further statistical processing.

Based on the findings described above, the following 47 chemical elements were discussed in statistical analyses: Ag, Al, As, Au, B, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Hf, Hg, In, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, S, Sb, Sc, Se, Sn, Sr, Te, Th, Ti, Tl, U, V, Y, Zn, Zr.

Methods for determination of geochemical threshold values

In scientific literature can be found several methods for calculating the geochemical threshold which is needed for recognizing the unusually high element concentrations. We summarize a selection of the most commonly used methods.

The original and most simple approach to calculate the geochemical threshold is “Mean + 2 × standard deviations (SD)” (abr. X2S) of a given data set. The method was developed in exploration geochemistry to detect data outliers and to determine the threshold between geochemical background and unusually high element concentrations that can indicate areas of mineralization (Matschullat et al., 2000; Reimann & Garrett, 2005; Reimann et al., 2018). The approach has many shortcomings, among which the most important one is that the method does not consider the multimodal nature of geochemical data sets (Reimann & Filzmoser, 2000).

sko vrednostjo elementa v vzorcu in njeno pripo- ročeno vrednostjo. Navadno primerjamo analitske vrednosti geoloških standardnih materialov z nji- hovimi priporočenimi vrednostmi (Abbey, 1983;

Reimann et al., 2009). Posamezni standardi so bili pod laboratorijskimi številkami naključno porazdeljeni med ostale vzorce in večkrat analizirani.

Izračunana razmerja med ponovitvami analiz in priporočenimi vrednostmi so pravzaprav poprav- ni količnik, s katerim bi morali deliti analizirane vrednosti, da bi se bolje približali priporočenim vsebnostim v vzorcih (Gosar, 2007).

Natančnost (P precision) analitike predstavlja mero ponovljivosti določanja nekega parametra v istem vzorcu ali v standardnem materialu ne glede na odstopanje od priporočene vrednosti (Rose et al., 1979; Reimann et al., 2009).

Kemične elemente Ge, Pd, Pt, Re, Ta in W smo izločili iz nadaljnje obdelave, ker je bila njihova vsebnost v več kot 30 % vzorcev nižja od spodnje meje zaznavnosti analitike (tabela 1). Za ostale elemente smo ugotovili, da so občutljivost, točnost (A accuracy) in natančnost (P precision) analitike zadovoljivi (tabela 1), zato smo rezultate vključili v nadaljnjo statistično obdelavo.

Na podlagi zgoraj navedenih ugotovitev smo v nadaljnjih statističnih obdelavah obravnavali naslednjih 47 elementov: Ag, Al, As, Au, B, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Hf, Hg, In, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, S, Sb, Sc, Se, Sn, Sr, Te, Th, Ti, Tl, U, V, Y, Zn, Zr.

Pregled metod za opredelitev zgornje meje naravne variabilnosti

V znanstveni literaturi najdemo več metod za določanje geokemičnih zgornjih mej naravne variabilnosti za določitev anomalno visokih vsebnosti elementov. V nadaljevanju povzemamo izbor najpogosteje uporabljenih metod.

Prvi in najpreprostejši pristop, pri katerem izračunamo zgornjo mejo naravne variabilnosti, temelji na izračunu “aritmetična sredina + 2 × standardni odklon (SD)” (okrajšano: X2S) za dane podatke. S tem izračunom so v preteklih geoke- mičnih raziskavah za iskanje mineralnih surovin računali vsebnost za definiranje meje med vsebnostmi, ki sodijo v geokemično ozadje in anomalno visokimi vsebnostmi, ki lahko nakazujejo območja mineralizacije (Matschullat et al., 2000; Reimann

& Garrett, 2005; Reimann et al., 2018). Ta metoda ima več pomanjkljivosti, med katerimi je najpo- membnejša neupoštevanje večmodalne narave ge- okemičnih podatkov (Reimann & Filzmoser, 2000).

Geokemični podatki so prostorsko variabilni, na njihove vrednosti vplivajo številni dejavniki

(9)

Unit DL UL N(DL) A (%) P (%)

Ag µg/kg 2 100000 817 1.6 17.2

Al % 0.01 10 817 3.0 10.7

As mg/kg 0.1 10000 817 0.2 8.7

Au mg/kg 0.2 100000 797 -1.5 43.1

B mg/kg 1 2000 631 5.0 21.5

Ba mg/kg 0.5 10000 817 -6.2 6.4

Be mg/kg 0.1 1000 814 0.7 20.8

Bi mg/kg 0.02 2000 817 1.3 14.4

Ca % 0.01 40 813 1.9 10.5

Cd mg/kg 0.01 2000 813 -1.8 13.6

Ce mg/kg 0.1 2000 817 -4.1 10.3

Co mg/kg 0.1 2000 817 0.9 6.7

Cr mg/kg 0.5 10000 817 1.9 7.8

Cs mg/kg 0.02 2000 817 2.5 14.3

Cu mg/kg 0.01 10000 817 -0.1 7.9

Fe % 0.01 40 817 2.2 4.3

Ga mg/kg 0.1 1000 817 3.2 8.1

Ge mg/kg 0.1 100 31 -13.9 1.0

Hf mg/kg 0.02 1000 658 0.6 22.8

Hg mg/kg 0.005 50 817 0.8 16.4

In mg/kg 0.02 1000 709 0.7 24.4

K % 0.01 10 816 2.6 13.3

La mg/kg 0.5 10000 817 2.2 10.3

Li mg/kg 0.1 2000 817 10.0 10.4

Mg % 0.01 30 817 1.0 7.3

Mn mg/kg 1 10000 817 -4.7 6.8

Mo mg/kg 0.01 2000 817 -6.0 10.3

Na % 0.001 5 806 7.0 13.7

Nb mg/kg 0.02 2000 816 -26.0 14.9

Ni mg/kg 0.1 10000 817 4.3 10.3

P % 0.001 5 817 1.5 6.0

Pb mg/kg 0.01 10000 817 -5.2 6.3

Pd µg/kg 10 100000 10 14.6 -

Pt µg/kg 2 100000 47 3.2 6.0

Rb mg/kg 0.1 2000 817 1.2 12.9

Re µg/kg 1 100 255 1.6 18.9

S % 0.02 5 668 9.3 13.5

Sb mg/kg 0.02 2000 817 -8.5 11.4

Sc mg/kg 0.1 100 817 13.8 10.5

Se mg/kg 0.1 100 778 -8.7 36.1

Sn mg/kg 0.1 100 817 0.1 14.4

Sr mg/kg 0.5 10000 817 -5.1 9.9

Ta mg/kg 0.05 2000 0 - -

Te mg/kg 0.02 1000 607 -5.5 41.8

Th mg/kg 0.1 2000 816 -0.2 12.3

Ti % 0.001 5 782 -1.4 15.0

Tl mg/kg 0.02 1000 817 1.4 7.6

U mg/kg 0.05 2000 817 -1.2 8.8

V mg/kg 2 10000 817 -1.3 7.4

W mg/kg 0.05 100 335 -38.6 10.5

Y mg/kg 0.01 2000 817 -3.3 7.7

Zn mg/kg 0.1 10000 817 -2.5 7.7

Zr mg/kg 0.1 2000 800 -12.0 14.0

DL – spodnja meja detekcije analiz/lower detection limit; UL – zgornja meja detekcije analiz/upper detection limit; N(DL) – število vzorcev nad DL/number of values above DL; A (%) – točnost/accuracy; P (%) – natančnost/precision

Table 1. Quality control of analytical method.

Tabela 1. Ocena kakovosti analitske metode.

(10)

Geochemical data have a high spatial variability, are influenced by many factors and are often imprecise due to unavoidable sampling errors, sample preparation and analytical errors.

Due to these properties of geochemical data, Re- imann et al. (2005) suggested to replace the earli- er X2S approach by using “Median + 2 × median absolute deviations (abr. MAD)” (abr. MD2MAD), where the median is defined for a sample x₁, … …, x_n as median_j(x_j). Median_i(x_i) is then defined for a new data set, that is determined from absolute values obtained by subtracting the median_j(x_j) from each original value in the sample. MAD is therefore determined as:

MAD_i(x_i) = 1.48 × median_i|x_i – median_j(x_j)|

In case of normal data distribution, a constant 1.48 is added to MAD definition for approxima- tion to standard deviation (SD) (Rousseeuw &

Croux, 1993; Reimann et al., 2018). The approach MD2MAD is much more efficient in exposing the anomalously high element concentrations, while the approach X2S only determines extreme values that are not necessarily the anomalous high values. The disadvantage of the MD2MAD method, if applied to raw, untransformed data, is that it delivers very conservative (low) threshold values (quite often around the 90^th percentile), i.e., it produces a lot of sites that need to be checked (Re- imann et al., 2018). The reason is that geochemical data distributions are most often strongly right- skewed, while, when using the above formula, the underlying assumption is of a symmetrical (not necessarily normal) data distribution. The cor- rect approach to using this formula would thus be to calculate “Median + 2 × MAD” (MD2MAD) on the log-transformed data (e.g., using log base 10) and then to back-transform the result and use these values as threshold according to the formula (Reimann et al., 2018). Geochemical threshold is therefore determined according to formula:

Threshold (after MD2MAD approach) = 10^b where

b = (median_i (log₁₀(x_i)) + 2 × MAD_j (log₁₀(x_j)) Values calculated using this approach are often comparable with the TIF (Tukey inner (upper) fence or upper whisker in a boxplot) method (Reimann et al., 2018). This method is based on the boxplot, an exploratory data analysis tool that depends solely on the symmetry of data dis-

in so pogosto nenatančni zaradi neizogibnih na- pak pri vzorčenju, pripravi vzorcev in analizah.

Reimann in sodelavci (2005) so glede na naštete lastnosti geokemičnih podatkov predlagali za- menjavo prej omenjenega pristopa z izračunom

“mediana + 2 × mediana absolutnih standardnih odklonov od mediane (okrajšano MAD)” (okraj- šano: MD2MAD), kjer je mediana definirana za podatke x₁, … …, x_n kot mediana_j(x_j). Nato se do- loči mediana_i(x_i) iz novega seta podatkov, ki se ga določi kot absolutna vrednost razlike med posa- mezno vrednostjo novega vzorca in mediane_j(x_j).

MAD je tako definiran kot:

MAD_i(x_i) = 1,48 × mediana_i|x_i – mediana_j(x_j)|

V primeru normalne porazdelitve podatkov je definicija MAD s konstanto 1,48 približek osnov- nemu standardnemu odklonu (SD) (Rousseeuw

& Croux, 1993; Reimann et al., 2018). Ta metoda veliko bolje izpostavi anomalno visoke vsebnosti.

Metoda X2S ugotovi predvsem samo ekstremne vrednosti, ki ne predstavljajo vedno tudi anomalno visokih vsebnosti. Pomanjkljivost metode MD- 2MAD je, da je ne smemo uporabiti na surovih, netransformiranih podatkih (Reimann et al., 2018). Pogoj za uporabo je namreč simetrična (in zlasti normalna) porazdeljenost podatkov. Če jo uporabimo za netransformirane podatke, dobimo kot rezultat zelo konzervativne (nizke) vrednosti zgornjih mej naravne variabilnosti, pogosto okoli 90. percentila. To je posledica desne asimetrič- nosti porazdelitve geokemičnih podatkov, ki je v geoloških materialih zelo pogosta. Obrazec za iz- račun predpostavlja osnovno simetrično (ne nujno normalno) porazdelitev podatkov. Zato je pravi- len pristop k uporabi te metode izračun “mediana + 2 × MAD” (MD2MAD) s transformiranimi podatki (npr. z uporabo logaritemske transforma- cije) ter ponovno re-transformacijo rezultata iz- računa (Reimann et al., 2018). V zadnjem primeru torej mejno vrednost za anomalno visoke vsebnosti z logaritemsko transformacijo izračunamo po obrazcu:

Zgornja meja naravne variacije (po metodi MD2MAD) = 10^b kjer je

b = (mediana_i (log₁₀(x_i)) + 2 × MAD_j (log₁₀(x_j)) Vrednosti, ki jih pridobimo s tem pristopom, so pogosto primerljive z metodo TIF (Tukeye- va notranja meja; ang. Tukey Inner Fence) (Rei-

(11)

tribution. It allows the definition of a threshold for outliers even if none are present in the data set (i.e., Max < TIF), as it extrapolates from the robust inner core (25^th to 75^th percentiles) of the data structure. TIF is calculated as follows:

TIF = Q3 + 1.5 × IQR

Where Q3 stands for the 3^rd quartile (equiva- lent to the 75^th percentile), and IQR is the interquartile range (75^th – 25^th percentile). The multi- plying factor of 1.5 in the formula is based on the assumption of a symmetrical data distribution.

All values higher than TIF are therefore labeled as anomalously high values. With this approach TIF also presents a geochemical threshold value (Reimann et al., 2005). When dealing with geochemical data, which are most often right- skewed, TIF must be calculated on the log- (or otherwise) transformed data to achieve “symmetry”. The TIF can be considered as one of the most reliable tools to calculate meaningful threshold values for any given data set (Reimann & Caritat, 2017; Reimann et al., 2018).

Reimann et al. (2005) compared methods discussed above by using normal distribution and log-normal distribution data sets. The results showed that the boxplot gives the best results when samples with anomalously high concentrations are less than 10 %. In case when samples with anomalously high concentrations exceed 15 % or even more than half of all data, only

“Median + 2 × MAD” (MD2MAD) gives useful results (Reimann et al., 2005). Approach “Mean + 2 × standard deviation” (X2S) exposes only extreme values. It is only meaningful when all samples with anomalously high concentrations also represent extreme values.

Another approach, which again stems from exploration geochemistry, is to study data distributions in a cumulative probability (CP) diagram (Reimann et al., 2018). A CP diagram shows statistical distributions of data and can detect processes that cause deviation from general data distribution (Reimann et al., 2005; Reimann et al., 2018). It allows detection of samples with anomalously high element concentrations and their distance from other data. Values above threshold are most often detected as a break of the distribution in the cumulative probability diagrams.

Geochemical threshold can also be determined by using the percentile-based approach (Reimann et al., 2018). It is a simplistic method with the 90^th, 95^th, 97.5^th or 98^th percentile of a given data set defining the threshold (abr. P90, P95, P97.5 and

mann et al., 2018). Ta metoda temelji na diagramu škatla z brki, ki omogoča določanje zgornjih mej naravne variabilnosti, tudi če med podatki ni vzorcev z anomalno visokimi vsebnostmi (torej je max < TIF). Izračuna se po sledečem obrazcu z ekstrapolacijo iz medkvartilnega razpona (25. do 75. percentil) vseh podatkov, kar predstavlja cen- tralno “škatlo”:

TIF = Q3 + 1,5 × IQR

Q3 je 3. kvartil (ekvivalent 75. percentilu) in IQR (Interquartile range) predstavlja medkvar- tilni razpon (75.–25. percentil). Faktor množenja 1,5 v formuli temelji na domnevi o simetrični po- razdelitvi podatkov. Vse vrednosti, ki so večje od tako postavljene meje, so anomalno visoke vsebnosti. S tem pristopom TIF predstavlja mejo naravne variabilnosti (Reimann et al., 2005). Tudi TIF mora biti v primeru desno asimetričnih geo- kemičnih podatkov izračunan preko log- (ali dru- gače) transformiranih podatkov, da se ti približa- jo “simetričnosti”. TIF je ena najbolj zanesljivih metod za izračun meje naravne variabilnosti za kakršnekoli podatke (Reimann & Caritat, 2017;

Reimann et al., 2018).

Reimann s sodelavci (2005) je primerjal nave- dene metode v primeru normalne porazdelitve in logaritemsko normalne porazdelitve. Rezultati so pokazali, da diagram škatla z brki poda najbolj- še rezultate v primeru, da je vzorcev z anomalno visokimi vsebnostmi manj kot 10 %. V primeru, da je vzorcev z anomalno visokimi vsebnostmi več kot 15 % ali celo več kot polovica vseh podatkov, da uporabne rezultate le metoda “mediana + 2 × MAD” (MD2MAD) (Reimann et al., 2005).

Metoda “aritmetična sredina + 2 × standardni odklon (X2S)” izpostavi le ekstremne vrednosti.

Smiselna je le v primeru, ko vsi vzorci z anomalno visokimi vsebnostmi predstavljajo hkrati tudi ekstreme.

Za določitev vzorcev z anomalno visokimi vsebnostmi se uporablja tudi grafični prikaz porazdelitve podatkov v diagramu kumulativne verjetnosti (CP) (Reimann et al., 2018). Diagram prikazuje statistične porazdelitve podatkov, iz katerih je mogoče zaznati procese, ki povzro- čajo odstopanje od splošne porazdelitve podatkov (Reimann et al., 2005; Reimann et al., 2018).

Omogoča določitev vzorcev z anomalno visokimi vsebnostmi ter njihovo oddaljenost od ostalih podatkov. Vrednosti nad zgornjo mejo naravne variacije se najpogosteje zazna kot prelom v porazdelitveni krivulji podatkov na teh diagra- mih.

(12)

P98, see also Ander et al., 2013). The 98^th percentile, which identifies 2 % of all samples as upper outliers, comes closest to the original method of calculating the “mean + 2 SD” in the case of a normal distribution, which would result in 2.3 % of upper outliers. A common feature of all these statistical methods is that it will not necessarily be possible to establish a meaningful single threshold valid for the whole country, because the background varies spatially. Furthermore, there exists no valid scientific reason why 2, 5 or 10 % of the samples should be considered as “anomalous” regardless of the statistical data distribution. It will only determine the highest values in a data set that may also be anomalous. Though the method is very practical due to its simplicity and as there is no need for normal data distribution and with it, for transformations.

Fig. 6. Basic lithological units (after data from Bavec et al. (2016) and Novak et al. (2016)).

Sl. 6. Osnovne litološke enote (po podatkih iz Bavec et al. (2016) in Novak et al. (2016)).

Zgornjo mejo naravne variabilnosti lahko dolo- čimo tudi z uporabo pristopa, ki temelji na percen- tilih (Reimann et al., 2018). Mejo lahko postavimo pri 90., 95., 97,5. ali 98. percentilu danih podatkov (okrajšano: P90, P95, P97.5 ter P98, glej tudi An- der et al., 2013). Osemindevetdeseti percentil, ki predstavlja 2 % najvišjih vrednosti, je najbližje iz- virni metodi računanja X2S v primeru normalne porazdelitve, ki bi v tem primeru določila 2,3 % vrednosti nad zgornjo mejo naravne variabilnosti.

S tem pristopom se lahko odkrije nesmiselno visoke mejne vrednosti naravne variabilnosti na zelo velikih območjih, saj ne upoštevajo vpliva ozadja, ki se prostorsko spreminja. Prav tako ne obstaja znanstveni razlog, zakaj bi moralo biti 2, 5 ali 10 % vzorcev določenih za “anomalne”. S temi metoda- mi se najenostavneje določi le najvišje vrednosti podatkov, ki so seveda lahko tudi anomalne. Meto-

(13)

In Great Britain, cumulative probability diagrams and percentiles (most frequently the 95^th percentile) have been used to detect samples de- viating from the “normal background deviation”

and to identify a case-specific threshold (e.g.

Cave et al., 2012; Johnson et al., 2012; Ander et al., 2013).

Geological and pedological settings and smaller spatial units in Slovenia Geological diversity of Slovenia is a result of a contact between 3 larger geotectonic units in Slovenia. Most of Slovenia is composed of clastic rocks and sediments that comprise around half of Slovenian area and carbonates (limestone and dolomites), that comprise around 40 % of the area. Metamorphic rocks cover around 4 % of the area, pyroclastic rocks are less than 2 % and around 1.5 % of Slovenian area is comprised of igneous rocks (Komac, 2005, fig. 6).

There are many soil types in Slovenia, as soil forming factors (lithology, relief, climate, hy-

Fig. 7. Smaller spatial units in Slovenija (addapted after Poljak, 1987).

Sl. 7. Prikaz opisanih prostorskih enot v Sloveniji (prirejeno po Poljaku, 1987).

da je vsekakor zelo praktična, ker je enostavna in ni potrebno, da so podatki normalno porazdeljeni.

Zato podatkov tudi ni potrebno transformirati.

V Veliki Britaniji pogosto uporabljajo diagra- me kumulativne verjetnosti in percentile (najpogosteje 95. percentil – P95) za ugotavljanje vzorcev, ki odstopajo od “normalne variacije v definiranem ozadju” in za določitev geokemične- ga praga (zgornjih mej naravne variabilnosti) na določenem območju (npr. Cave et al., 2012; John- son et al., 2012; Ander et al., 2013).

Geološke in pedološke značilnosti Slovenije in razmejitev na manjše prostorske enote Slovenija leži na ozemlju stika 3 velikih geotek- tonskih enot in je zato geološko zelo pestra. Velik del Slovenije sestavljajo klastične kamnine in sedimenti, ki obsegajo približno polovico površine ozemlja Slovenije ter karbonati (apnenci in dolomiti), ki jih je okoli 40 %. Metamorfne kamnine obsegajo približno 4 % površine slovenskega ozemlja, piroklastičnih kamnin je manj kot 2 %, najmanj

(14)

drology, vegetation, organisms and human influence) have a large spatial variability (Vidic et al., 2015). In Slovenia, lithology and relief have the greatest impact on soil formation (Vidic et al., 2015). Most of Slovenia is comprised of carbonate rocks, (including sediments and clastic rocks with carbonate clasts or cement) and soils that forms on these rocks. Lithosols and shallow rendzinas are present on steep slopes in mountain terrains. In areas with gentle slopes, brown soils on limestone and dolomite and rendzinas are present (Vidic et al., 2015). On carbonate flysch in western Slovenia and marlstones in eastern Slovenia are rendzinas and eutric brown soils. Rendzinas are also common on other clastic carbonate sediments as gravel and sand in river valleys (Vidic et al., 2015). Dystric rankers, dystric brown soils and leached soils are present on other noncarbonate clastic rocks and most of metamorphic and igneous rocks. On noncarbonate sediments in valleys of eastern Slovenia (Drava and Ptuj plains, Prekmurje), dystric soils are formed (Vidic et al., 2015).

Next, we present the spatial distribution of Slovenia, that is based on geological structure, lithology, relief, climate and vegetation acocording to suggestions from Poljak (1987).

Slovenia was divided into 8 smaller spatial units (Western Alps, Eastern Alps, Western Prealps, Eastern Prealps. Western Dinarides, Eastern Dinarides, Pannonian basin and Inte- rior basins, fig. 7), that we discuss later. Nam- ing of spatial units is valid only for Slovenia and is not related to other units’ names in Eu- rope or in the entire Alps. Geological settings are summarized after Geology of Slovenia (Pleničar et al., 2009) and pedological settings after monograph Soils of Slovenia with soil map 1 : 250,000 (Vidic et al., 2015).

Table 2. Smaller spatial units in Slovenia and number of samples for each unit (N).

Tabela 2. Prostorske enote v Sloveniji in število pripadajočih vzorcev tal (N).

Prostorske enote – Spatial units N

Zahodne Alpe (Western Alps) 99

Vzhodne Alpe (Eastern Alps) 80

Zahodne Predalpe (Western Prealps) 98

Vzhodne Predalpe (Eastern Prealps) 116

Zahodni Dinaridi (Western Dinarides) 66

Vzhodni Dinaridi (Eastern Dinarides) 163

Panonska nižina (Pannonian basin) 157

Notranje kotline (Interior basins) 38

(1,5 %) pa je magmatskih kamnin (Komac, 2005, sl. 6). Ker so v Sloveniji tlotvorni dejavniki, torej matična osnova, relief, klima, vodne razmere, ra- stlinske združbe, dejavnost organizmov in aktiv- nosti človeka, močno spremenljivi in se pojavljajo v različnih kombinacijah, je tudi talna odeja zelo pestra (Vidic et al., 2015). Največji vpliv na nastaja- nje tal, in s tem tudi na pestrost talnih tipov v Slo- veniji, imata matična podlaga in relief (Vidic et al., 2015). Za Slovenijo je najbolj značilna karbonatna podlaga (karbonatne kamnine ter sedimenti in se- dimentne kamnine, ki vsebujejo karbonatna zrna ali vezivo) ter tla, ki se tam razvijajo. V visokogor- skih območjih in na strmih pobočjih najdemo lito- sole in plitve rendzine. V nižjih predelih in na manj strmih pobočjih pa nastopajo skupaj z rendzinami tudi rjava pokarbonatna tla (Vidic et al., 2015). Na karbonatnem flišu zahodne Slovenije in laporovcih vzhodne Slovenije prevladujejo rendzine in evtrič- na rjava tla. Rendzine se pojavljajo tudi na drugih klastičnih karbonatnih sedimentih, kot so prodi in peski v nekaterih rečnih dolinah (Vidic et al., 2015).

Na drugih nekarbonatnih klastičnih kamninah, na večini metamorfnih in magmatskih kamnin so di- strični rankerji, distrična rjava tla in rjava izprana tla. V nižinah vzhodne Slovenije (Dravsko, Ptujsko polje, Prekmurje) so na nekarbonatnih sedimentih razvita distrična tla (Vidic et al., 2015).

V nadaljevanju povzemamo prostorsko razde- litev Slovenije, ki smo jo izvedli na podlagi geolo- ške zgradbe, kamninske sestave (litologije), reliefa, podnebja in rastlinstva skladno s predlogi Polja- ka (1987). Slovenijo smo razdelili na 8 manjših prostorskih enot (Zahodne Alpe, Vzhodne Alpe, Zahodne Predalpe, Vzhodne Predalpe, Zahod- ni Dinaridi, Vzhodni Dinaridi, Panonska nižina, Notranje kotline, sl. 7), katerih značilnosti poda- jamo v nadaljevanju. Prostorske enote smo določili in poimenovali samo za Slovenijo in poimenovanje nima enakega pomena kot v Evropi in celotnih Al- pah. Geološke značilnosti so povzete iz monografi-

(15)

Samples were collected at a grid of 5 × 5 km and assigned to spatial units according to their spatial distribution in Slovenia. Number of samples (N) in each spatial unit is presented in table 2.

Western Alps

Western Alps cover the area of the Julian Alps, Kamnik Savinja Alps and Karavanke Alps (Karawanks) in which are the highest peaks of Slovenia. Most of this spatial unit comprises area above the tree line, which is reflected in almost no vegetation and shallow (rendzinas) or unde- veloped soils. Julian and Kamnik Savinja Alps are predominantly composed of carbonate rocks.

The area developed from glacial processes and is influenced by dissolution of limestone in karstic areas. Due to lithology and dissolution of limestone, this area is also called “high or Alpine karst”. The Karavanke Alps are a long mountain range along Austrian border which ends near Mežica in the east. Their lithology is diverse, with carbonate rocks, which predominate, clastic and igneous rocks.

The Julian Alps are mostly composed of limestone and dolomite (Dozet & Buser, 2009). Lime- stone with chert, clay marlstone and limestone with manganese nodules are also present (Buser

& Dozet, 2009). Larger areas containing iron ore are in the vicinity of Pokljuka, Bohinj and Jelovi- ca (Ogorelec et al., 2006; Pirc & Herlec, 2009).

There are smaller areas of Cretaceous flysch marlstone, located in southern part of the Julian Alps (Pleničar, 2009).

The Kamnik Savinja Alps consist of mostly carbonate rocks and less clastic rocks (Dozet and Buser, 2009). Carbonate conglomerates that transit to marly clay called “sivica” are present on larger area of Smrekovec and Gorn- ji Grad. There are Oligocene volcaniclastic rocks on Smrekovec area and other smaller areas in the Kamnik Savinja Alps (Pavšič & Hor- vat, 2009).

Carbonate rocks (limestones and dolomites) predominate in the Karavanke Alps. Clastic rocks are also common. Iron, lead, zinc, and an- timony ores often occur at the contact of clastic rocks and carbonate rocks (Ramovš & Buser, 2009; Novak & Skaberne, 2009). Igneous rocks, limestone with chert and shales are also present in the Karavanke Alps. Also found in the Kara- vanke Alps are traces of manganese ore, that was mined in this area.

je Geologija Slovenije (Pleničar et al., 2009), pedo- loške pa po monografiji Tla Slovenije s pedološko karto v merilu 1 : 250 000 (Vidic et al., 2015).

Vzorčna mesta tal, ki so bila vzorčena v mre- ži 5 × 5 km, smo v skladu s prikazano prostorsko porazdelitvijo Slovenije, pripisali posameznim prostorskim enotam. V tabeli 2 je navedeno število vzorcev tal (N), odvzetih v posamezni prostorski enoti.

Zahodne Alpe

Zahodne Alpe obsegajo Julijske Alpe, Kam- niško Savinjske Alpe in Karavanke, ki reliefno predstavljajo najvišje vrhove v Sloveniji. Večji del te prostorske enote obsega območja nad gozdno mejo, torej je vegetacija skromna, tla so večinoma plitva (rendzine) ali nerazvita. Območje Julijskih in Kamniško Savinjskih Alp je zgrajeno pretežno iz karbonatnih kamnin. To območje se je oblikova- lo z ledeniškim delovanjem in je podvrženo recen- tni kraški eroziji. Zaradi litološke sestave in kraške erozije se območje Alp imenuje tudi “visoki ali alp- ski kras”. Karavanke predstavlja dolg gorski gre- ben, ki se vleče v ozkem pasu ob avstrijski meji do Mežice na vzhodu. Litološka sestava je pestra. Pre- vladujejo čiste karbonatne kamnine. Najdemo pa tudi raznovrstne klastične in magmatske kamnine.

Julijske Alpe so v večini sestavljene iz apnen- cev in dolomitov (Dozet & Buser, 2009). Mestoma najdemo apnence z roženci, glinene laporovce ter apnence z manganovimi gomolji (Buser & Dozet, 2009). V okolici Pokljuke – Bohinja ter Jelovice so pomembnejša orudenja železa (Ogorelec et al., 2006; Pirc & Herlec, 2009). V južnem delu Julij- skih Alp so manjša območja krednega flišnega la- porovca (Pleničar, 2009).

Kamniško Savinjske Alpe sestavljajo večino- ma karbonatne kamnine, mestoma izdanjajo tudi klastiti (Dozet & Buser, 2009). Na širšem območju Smrekovca in Gornjega Grada ležijo karbonatni konglomerati, ki prehajajo v laporasto glino ali sivico. Na območju Smrekovca ter v manjših ob- močjih znotraj Kamniško Savinjskih Alp najdemo oligocenske vulkanoklastične kamnine (Pavšič &

Horvat, 2009).

V Karavankah prevladujejo karbonatne kamnine (apnenci in dolomiti) različnih starosti. Po- goste so tudi klastične kamnine, kjer se mestoma na njihovem stiku z apnencem pojavljajo orudenja železa, svinca, cinka in antimona (Ramovš & Bu- ser, 2009; Novak & Skaberne, 2009). V Karavan- kah se mestoma pojavljajo magmatske kamnine.

Ponekod najdemo apnence z roženci, skrilave gli- navce in sledove manganove rude, ki so jo v prete- klosti kratek čas tudi izkoriščali.

(16)

Rendzinas (profile A-C or A-R) predominates on carbonate rocks at higher altitudes and steep slopes. Rarely, brown soils (A-B-C) have formed.

Dystric brown soils have formed on clastic and volcaniclastic rocks (Vidic et al., 2015).

Eastern Alps

Eastern Alps cover the area of Pohorje, a large massif that is distinctly separated from other areas.

This area is predominantly composed of igneous and metamorphic rocks. Weathering of these rocks causes forming of dystric brown soils and rankers on steeper slopes (Vidic et al., 2015). Pohorje is covered with dense vegetation with conifers, mixed forests and meadows due to fertile soils, wet climate and impermeable lithology.

Central part of the Pohorje range is composed of granodiorite batholith, that is surrounded with metamorphic rocks, of which most special are eclogite and garnet peridotite. In northern part of Pohorje there are mostly mica schists, gneisses, amphibolite and less eclogite and marble. Amphibolite that includes chlorite and epidote is found in southwestern part of Pohorje (Hinterlechner-Ravnik & Trajanova, 2009).

Phyllitic schists with quartzite, metakerato- phyre, marble, graphitic slates and amphibole schists with chlorite and epidote represent the transit from lower grade metamorphic rocks to higher metamorphic grade, found west of Koban- sko. Mineral garnet is more common (Hinter- lechner-Ravnik & Trajanova, 2009). Miocene conglomerate with dacite tuff is present in the Ribnica-Selnica tectonic graben and on Mt. Koz- jak (Pavšič & Horvat, 2009).

Quartz sandstones, conglomerates and siltstones compose western part of Eastern Alps.

Dolomites, limestones, marlstones and claystone are present in smaller areas (Dozet & Buser, 2009;

Buser & Dozet, 2009). In the area around Stran- ice and Zreče are dolomites with layers of black coal, claystone, siltstones and marlstones. In Ve- lenje valley predominate Plio-Quaternary clastic rocks and sediments that include carbonates and pyroclastic rocks with andesite and dacite.

Here are layers of lignite between clastic rocks (Markič, 2009).

Igneous rocks of Pohorje are divided into two groups: Magdalensberg series and Železna Kap- la magmatic zone. Magdalensberg series passes along river Meža, via Slovenj Gradec to north- western Pohorje and area of Remšnik. Central part of the series is composed of felsic igneous

Na karbonatnih kamninah v višjih legah z nekoliko strmejšim reliefom je prevladujoči talni tip rendzina (profil A-C ali A-R), ki le mestoma prehaja v rjava pokarbonatna tla (A-B-C). Na klastičnih in vulkanoklastičnih kamninah so di- strična rjava tla (Vidic et al., 2015).

Vzhodne Alpe

Vzhodne Alpe obsegajo Pohorje, velik masiv, ki se ostro loči od sosednjih ozemelj.

Gradijo ga pretežno magmatske in metamorfne kamnine, iz katerih pri preperevanju nastajajo na strmejših delih rankerji, bolj pogosto pa dis- trična rjava tla (Vidic et al., 2015). Večinoma so to rodovitna tla, ki omogočajo ob obilju padavin in nepropustni geološki podlagi gost vegetacijski pokrov iglavcev, mešanega gozda in travnikov.

Na osrednjem delu grebena Pohorja je gra- nodioritni batolit, ki ga obdajajo metamorfne kamnine. Posebnosti sta eklogit in granatov pe- ridotit. Na severnem delu Pohorja najdemo predvsem blestnik, gnajs in amfibolit, manj je eklogita in marmorja. Amfibolit je tudi na jugozahodnem delu Pohorja, ki tu vključuje klorit in epidot (Hin- terlechner-Ravnik & Trajanova, 2009).

Zahodno od Kobanskega so razvite manj metamorfozirane kamnine, ki prehajajo v močneje metamorfozirane kamnine, ki jih predstavljajo filitni skrilavci s kvarcitom, metakeratofir, mar- mor in grafitni skrilavec ter amfibolovi skrilavci s kloritom in epidotom. Na nekaterih območjih je zelo pogost mineral granat (Hinterlechner-Rav- nik & Trajanova, 2009). Na Kozjaku ter v Rib- niško-selniškem tektonskem jarku so miocenski konglomerati, ki vsebujejo tudi dacitne tufe (Pavšič & Horvat, 2009).

Na zahodnem območju Vzhodnih Alp so kre- menovi peščenjaki, konglomerati in meljevci.

Mestoma se pojavljajo dolomiti, apnenci, laporovci in glinavci (Dozet & Buser, 2009; Buser & Do- zet, 2009). V okolici Stranic in Zreč so dolomiti s plastmi črnega premoga ter glinavci, meljevci in laporovci. V Velenjski kotlini je veliko plio- kvartarnih klastitov, ki izvirajo iz podlage in jih zastopajo karbonati ter piroklastične kamnine z andezitom in dacitom, med njimi pa je prisoten lignit (Markič, 2009).

Magmatske kamnine na Pohorju izdanjajo na območju štalenskogorske serije in železnokapel- ske magmatske cone. Štalenskogorska serija po- teka vzdolž reke Meže preko Slovenj Gradca na severozahodno Pohorje ter na območje Remšnika.

Kisle magmatske kamnine sestavljajo osrednji del serije, na severnem delu serije pa so bazične magmatske kamnine (Trajanova, 2009). Železno-